Ultra-low dose computed tomography protocols using spectral shaping for lung cancer screening: Comparison with low-dose for volumetric LungRADS classification

医学 肺癌 计算机断层摄影术 核医学 肺癌筛查 断层摄影术 放射科 内科学
作者
Gianluca Milanese,Roberta Eufrasia Ledda,Federica Sabia,Margherita Ruggirello,Stefano Sestini,Mario Silva,Nicola Sverzellati,Alfonso Marchianò,Ugo Pastorino
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:161: 110760-110760 被引量:11
标识
DOI:10.1016/j.ejrad.2023.110760
摘要

To compare Low-Dose Computed Tomography (LDCT) with four different Ultra-Low-Dose Computed Tomography (ULDCT) protocols for PN classification according to the Lung Reporting and Data System (LungRADS).Three hundred sixty-one participants of an ongoing lung cancer screening (LCS) underwent single-breath-hold double chest Computed Tomography (CT), including LDCT (120kVp, 25mAs; CTDIvol 1,62 mGy) and one ULDCT among: fully automated exposure control ("ULDCT1"); fixed tube-voltage and current according to patient size ("ULDCT2"); hybrid approach with fixed tube-voltage ("ULDCT3") and tube current automated exposure control ("ULDCT4"). Two radiologists (R1, R2) assessed LungRADS 2022 categories on LDCT, and then after 2 weeks on ULDCT using two different kernels (R1: Qr49ADMIRE 4; R2: Br49ADMIRE 3). Intra-subject agreement for LungRADS categories between LDCT and ULDCT was measured by the k-Cohen Index with Fleiss-Cohen weights.LDCT-dominant PNs were detected in ULDCT in 87 % of cases on Qr49ADMIRE 4 and 88 % on Br49ADMIRE 3. The intra-subject agreement was: κULDCT1 = 0.89 [95 %CI 0.82-0.96]; κULDCT2 = 0.90 [0.81-0.98]; κULDCT3 = 0.91 [0.84-0.99]; κULDCT4 = 0.88 [0.78-0.97] on Qr49ADMIRE 4, and κULDCT1 = 0.88 [0.80-0.95]; κULDCT2 = 0.91 [0.86-0.96]; κULDCT3 = 0.87 [0.78-0.95]; and κULDCT4 = 0.88 [0.82-0.94] on Br49ADMIRE 3. LDCT classified as LungRADS 4B were correctly identified as LungRADS 4B at ULDCT3, with the lowest radiation exposure among the tested protocols (median effective doses were 0.31, 0.36, 0.27 and 0.37 mSv for ULDCT1, ULDCT2, ULDCT3, and ULDCT4, respectively).ULDCT by spectral shaping allows the detection and characterization of PNs with an excellent agreement with LDCT and can be proposed as a feasible approach in LCS.

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